pip install twine==3.6.0
fi

# When cross-compiling with RBE, we need to copy the macOS sysroot to be
# inside the TensorFlow root directory. We then define them as a filegroup
# target inside "tensorflow/tools/toolchains/cross_compile/cc" so that Bazel
# can register it as an input to compile/link actions and send it to the remote
# VMs when needed.

./mc cat minio2/test-bucket/custpartsize --enc-c "minio2/test-bucket/custpartsize=${TEST_MINIO_ENC_KEY}" --insecure >/dev/null || exit_1

# Check the MD5 checksums of encrypted objects from source and target
if [ "${src_obj1_md5}" != "${rep_obj1_md5}" ]; then
	echo "BUG: MD5 checksum of object 'minio2/test-bucket/encrypted' doesn't match with source. Expected: '${src_obj1_md5}', Found: '${rep_obj1_md5}'"
	exit_1
fi

			h.ServeHTTP(w, r)
			return
		}

		// CopyObject requests should be handled at current endpoint as path style
		// requests have target bucket and object in URI and source details are in
		// header fields
		if r.Method == http.MethodPut && r.Header.Get(xhttp.AmzCopySource) != "" {
			bucket, object = path2BucketObject(r.Header.Get(xhttp.AmzCopySource))

	opts.ReplicationSourceTaggingTimestamp = taggingtimestmp
	opts.PreserveETag = etag

	return opts, nil
}

// get ObjectOptions for Copy calls with encryption headers provided on the target side and source side metadata
func copyDstOpts(ctx context.Context, r *http.Request, bucket, object string, metadata map[string]string) (opts ObjectOptions, err error) {
	return putOptsFromReq(ctx, r, bucket, object, metadata)
}

		// SetRemoteTargetHandler
		adminRouter.Methods(http.MethodPut).Path(adminVersion+"/set-remote-target").HandlerFunc(
			adminMiddleware(adminAPI.SetRemoteTargetHandler)).Queries("bucket", "{bucket:.*}")
		// RemoveRemoteTargetHandler
		adminRouter.Methods(http.MethodDelete).Path(adminVersion+"/remove-remote-target").HandlerFunc(
			adminMiddleware(adminAPI.RemoveRemoteTargetHandler)).Queries("bucket", "{bucket:.*}", "arn", "{arn:.*}")

			return ErrAdminConfigIDPCfgNameAlreadyExists
		}

		return ErrNone
	}

	// For the default configuration name, since it will always be an available
	// target, we need to check if a configuration value has been set previously
	// to figure out if this is a valid create or update API call.

	// This cannot really error (FIXME: improve the type for GetConfigInfo)

ways to detect malicious models/graphs/checkpoints, so the recommended way to
mitigate the risk in this scenario is to sandbox the model execution.

### Hardware attacks

Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other

	echo "BUG: ILM expiry rules tags not replicated to 'sited'"
	exit 1
fi

## Check replication of deleted ILM expiry rules when target has transition part as well
## Only the expiry part of rules should get removed as part if replication of removal from
## other site

        "//tensorflow/core:testlib",
        "@com_google_absl//absl/strings",
    ],
)

# -----------------------------------------------------------------------------
# Python API target

tf_cuda_library(
    name = "python_api",
    srcs = ["python_api.cc"],
    hdrs = ["python_api.h"],
    visibility = [
        "//tensorflow:internal",
        "//tensorflow/python:__pkg__",

If the DNS SRV record is at an entirely different place, say `_ldapsrv._tcpish.myldapserver.com`, then set `srv_record_name` to the special value `on` and set `server_addr=_ldapsrv._tcpish.myldapserver.com`.